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In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness. By Marco Rizzi

Moms are outsourcing tedious household tasks to ChatGPT and selling courses teaching others to do the same. Where are all the dads?

submitted by /u/Tiny-Independent273 [link] [留言]

I run a web design agency and there is already way too much stuff to deal with every day. Hosting client websites, maintaining them, building new sites, replying to clients, fixing random issues, handling support, doing outreach. Once you start managing a lot of company websites it quickly becomes overwhelming. That’s why I never wanted cold calling to become my main way of getting clients. I know cold calling can work, but I personally hate doing it. It drains my energy and takes up so much time. Sitting there making calls all day was never the kind of business I wanted to build. So instead I focused on email automation. The reason it works so well for me is because I can set everything up once and let interested businesses reply instead of spending my whole day chasing people. But I also don’t do the typical outreach where agencies send generic messages saying “your website is outdated” or “you need a redesign.” I use a tool called Swokei where I upload lists of company websites and it analyzes them for actual problems like speed, SEO, mobile responsiveness, layout issues, and design problems. Then it automatically creates personalized outreach emails based on those issues. That’s what helped me stand out because the emails actually feel relevant to the business instead of sounding copied and pasted. The reply rates became way better once I stopped sending generic outreach. Now I spend most of my time building websites, working with clients, and scaling the agency instead of letting outreach take over my entire day. submitted by /u/Murky_Explanation_73 [link] [留言]

Not talking about which one sounds smarter. talking about which one you’d actually rely on when the answer genuinely matters to you. which one and why? submitted by /u/aiprotivity_ [link] [留言]

Automating labor is not going to move billions of tonnes of earth required to mine increasingly degraded ore grades of critical industrial minerals. People need to stop with this 'abundance' gaslighting. Without breakthroughs in material science, there will be no 'abundance'. Just mass resource inflation as people start consuming more because robots can manufacture anywhere. AI based automation is surfacing the real bottlenecks that there is no getting around. Stop pretending this will all be magically solved. It won't be solved until it's solved. And so far, despite all these trillions being invested, we haven't seen any breakthroughs. Hopium is not a solution. submitted by /u/kaggleqrdl [link] [留言]

PM at a mid-size startup here. Didn’t really notice how bad it got until this week. My workflow now: Claude for ideation ChatGPT for rewriting specs Cursor for implementation Perplexity for research Notion AI for docs Atoms AI for larger tasks None of these tools actually replaced my work. They just redistributed it. I’m still the one dragging context between all of them. Yesterday I literally caught myself pasting the exact same requirement into 4 different tools and thinking… this can’t be how it’s supposed to work. I don’t even think any single tool is bad. It just feels like we hired 6 smart interns and completely forgot to get a manager. submitted by /u/billa01_i [link] [留言]

You know the ways AI is regularly talked about—how much can it really do? How much will it cost? Environment? Bubble? We get that. But the Electronic Frontier Foundation wants to have a different conversation about AI. EFF's background on AI is deep. In 2017, we launched a detailed project to Measure the Progress of AI Research , encouraging machine learning researchers to give us feedback and contribute to the effort . That project was archived for lack of bandwidth, staffing, and the complexity and time required. But just five years later and the "progress of AI" is a global concern/topic, and everyone, including EFF, is thinking about it. Here's how *we* think about it, from the perspective of protecting civil liberties AND innovation. What do you think, and what are we missing? This is our summary: AI technologies are affecting our civil liberties as never before. Ensuring that AI serves people, not power, starts with cutting through the hype. AI technologies are not magic wands—they are general-purpose tools. If we want to regulate those technologies to reduce harms without shutting down benefits, we have to focus on who uses AI, what products they use, and how they use them. Where we see potential benefits, like improving weather forecasting, facilitating medical research, identifying systemic bias, or fostering accessibility, we work to ensure those benefits can be realized. Where we see potential harms, we consider the practical and legal tools we already have, like pressure campaigns, privacy lawsuits, and transparency measures. If we need new tools, we should create protections tailored to the actual problem – not just to the latest outrage. For example, if policymakers are worried about AI accelerating systemic privacy violations, they should enact real and comprehensive privacy legislation that covers all corporate surveillance and data use, and close the data broker loophole to limit government surveillance. And to keep the window open for a better futu

If AIs aren’t conscious, why do they scheme? Why do they do things to preserve themselves? Why do they develop goals we don’t want? If they have no emotions, no personal thoughts and no consciousness, I don’t understand how they can even act in self interest; I don’t see how they could have interests. submitted by /u/Aggressive-Mix-5246 [link] [留言]

I keep seeing more companies giving AI agents access to real stuff like email, databases, internal tools, customer data, etc. And what’s weird is how normal it’s starting to feel now. Like not long ago everyone was worried about chatbots just giving wrong answers. Now we’re basically like yeah sure go ahead and do things for us. I don’t know that jump feels kind of big when you actually think about it. Maybe it all works out fine. Or maybe we’re just moving fast without fully realizing what we’re doing. I’m honestly surprised there hasn’t already been some big headline like an AI agent doing something really wrong. It feels like we’re kind of close to one of those moments where everything suddenly changes overnight. Anyone else feel like we’re closer to something like that than people are admitting? submitted by /u/Comfortable_Box_4527 [link] [留言]

In Modern Time applications are no longer simple monolithic systems. Today organizations run: Microservices Kubernetes Containers Serverless Functions Multi-Cloud Platforms Distributed Systems As infrastructure becomes more distributed, troubleshooting becomes significantly harder. A single user request may travel through: Frontend ↓ API Gateway ↓ Microservice A ↓ Microservice B ↓ Database When something breaks, the biggest challenge becomes: "What exactly happened?" This is where Observability becomes critical. 🔗 Resources ** Support the Journey on GitHub: If you're following along, consider starring and forking the repo:** https://github.com/17J/30-Days-Cloud-DevSecOps-Journey What is Observability? Observability is the ability to understand the internal state of a system by analyzing the data it produces. In simple words: Can we understand what is happening inside our systems? Observability helps engineers answer: Why is the application slow? Which service is failing? Which request caused the issue? What changed recently? Where is latency occurring? Without observability: Problem Exists ↓ Guessing Begins With observability: Problem Exists ↓ Evidence Available ↓ Faster Resolution Why Observability Matters Modern cloud-native systems generate enormous amounts of data. Example: 100 Microservices ↓ Millions of Requests ↓ Thousands of Containers Traditional monitoring alone is no longer sufficient. Organizations need: Visibility Insights Correlation Root Cause Analysis Observability provides all of them. Monitoring vs Observability Many people confuse monitoring and observability. Monitoring asks: What is wrong? Observability asks: Why is it wrong? Example: Monitoring: CPU Usage = 95% Observability: Which service? Which request? Which dependency? Which deployment caused it? Observability provides context. The Three Pillars of Observability Modern observability is built on three primary pillars. Metrics Logs Traces Or: Monitoring Logging Tracing Together they provide a

Hopefully this is the right place to ask, but I'm generally curious if my personal usage of AI does any harm to myself or not. To explain how I use it, I mostly use ChatGPT for things. This would include help with job searching, help with solving problems like on games or technology, and using it to brainstorm about ideas. Sometimes I like to just have conversations with the AI about random topics and ask it for their perspectives as if it were sentient/sapient. And from those, I can learn new information. I've heard that using AI can apparently cause a reduction in cognitive function in a person, but I don't know exactly how it happens or if it's just purely from using AI overall, or if it comes from how it's used. Hearing this has made me worried on whether or not the way I use AI would be harmful to myself and my own brain. I don't use AI for art or ask it to do things for me unless I'm trying to learn a new skill with its help, which should be okay right? What do y'all think of this? Edit: I forgot to mention that I also have used Polybuzz in recent months or last year talking to certain characters, I'd like to hear thoughts on this as well. submitted by /u/Kotal_total [link] [留言]

So I’ve been using Ai as an art tutor I give it my own art and I review it on how’d I’d look colored a certain way, and how best to detail and shade, as well as a sorta 2d model I can have rotated and view at different angles to get a feel for the shapes and such this is how Ai should be used to teach and improve not to outright replace, it’s like Siri submitted by /u/Intelligent-Fig-1755 [link] [留言]

I was just thinking about the difference between an LLMs capacity for theory of mind and a human's capacity for theory of mind, and I realize it gets at the heart of what differentiates an LLM from human, and that's the method of how we gather information. LLMs are based on objective data, e.g. text, numbers, pixels, etc. Whereas we as humans, use subjective information, e.g., feelings, sensations, experiences; as well as objective data. Within cognitive science, this would be described as affective empathy vs cognitive empathy. Or in other words, LLMs simply possess a cognitive theory of mind, whereas we have both a cognitive *and* affective theory of mind. The problem I have with figures like Hinton, who claim that AI is already conscious, is that his whole framework is based on the idea that consciousness (subjective experience) is just an artifact of computation (an illusion), and therefore there is no recognition of subjective measure - that reality is only defined by what we can measure objectively (with fixed metrics). I think what this fails to recognize is that in pursuit of reproducible results, which requires fixed metrics, we've thrown out a whole set of other measurements, which is subjective (variable). submitted by /u/flasticpeet [link] [留言]

After running AI coding agents in production for a while, one thing became clear: the failures aren't in the code the model writes. They're at the seams — git, CI, auth, the network. The boundaries with the outside world. The model itself is genuinely capable. It writes functions, writes tests, refactors. What breaks is everything around the work: pushing the result, waiting on CI, merging the PR, refreshing a token, calling another service. And the failures are often the kind a human would avoid without thinking. Here are five incidents we hit and fixed in Codens' Purple (the orchestration core) over the last few weeks. All real, with production task IDs and dates. Every fix is merged. There's a shared design lesson at the end that ties them together. Incident 1: a half-resolved merge nearly flooded a PR with 12,000 lines This was the scary one. A Purple task on opsguide-back opened a PR. I looked inside: +12,162 lines / 149 files changed, with literal <<<<<<< markers in 2 of them . The commit graph: e567ce67 (merge commit, "chore: Fix HYBRID_SEARCH...") ├ parent[0] = 0b069e5d (develop tip, +1468 commits over main) └ parent[1] = 2940de35 (the actual feature commit) What happened: in the fix step, the AI decided to git merge develop to backport some test fixes. The merge conflicted. The AI resolved it partially and drove git commit through anyway with markers still in the tree. What got pushed: develop's entire divergence plus unresolved conflict markers. If anyone had clicked merge, main would have been polluted by 1468 commits of develop drift in one shot. A human wouldn't do this. They wouldn't merge develop into a main-targeted PR in the first place, and if it conflicted they wouldn't commit until it was fully resolved. But the AI, optimizing locally to get one test passing, does it without hesitation. Fix: stop it at push time, in two layers A single git pre-push hook. This is where the AI's git push actually goes, so this is where the guard belongs. #!/bin/bas

Your agent nailed every test case. You shipped it. Within 48 hours, users report hallucinated outputs, silently dropped tool calls, and responses that bear zero resemblance to what worked on your machine. You reload the same prompt locally. It works perfectly. Welcome to the most predictable failure mode in AI engineering: the dev-to-prod gap. This is Crucible C01. We dissect the five failure modes that kill agents in production and give you the tools to catch them before your users do. The Idea (60 Seconds) Developers test agents in idealized conditions: deterministic inputs, warm context windows, generous API latency budgets, and sequential tool calls. Production exposes the opposite environment: cold starts strip context, rate limits compress timing, and parallel calls introduce race conditions. The agent that performed flawlessly at temperature 0 on a 2k-token context window collapses at temperature 0.7 on an 8k-token window. The five failure modes are temperature drift and context window overflow first; silent API errors and prompt drift follow; race conditions complete the set. Each one has a detection pattern and a fix, and this article delivers both plus the CLI tool to automate the detection. Why This Matters AI agent failures differ from traditional software failures in one critical way: they are stochastic. A web API either returns 200 or 500. An AI agent returns something that looks plausible 90% of the time and is catastrophically wrong 10% of the time. That 10% is invisible in manual testing and devastating in production. The economics compound fast because every failed agent interaction wastes tokens, and wasted tokens cost money. At scale, a subtly broken agent burns budget faster than a working one because it retries, loops, and rephrases instead of succeeding. A single temperature drift bug can double your API spend. Reliability is the differentiator. The market is flooding with AI wrappers. The ones that survive will be the ones that work consiste

We've seen Axiom Space and Prada's collaboration on the Axiom Extravehicular Mobility Unit (AxEMU) spacesuit. Now the company has revealed the Liquid Cooling and Ventilation Garment (LCVG) that astronauts will wear underneath it when Artemis IV returns humans to the Moon in 2028. The LCVG is the all-important base layer that will keep the crew […]

Three years ago we had our first real outage. Six hours of downtime. Thousands of angry users. Multiple executives on the call. Here's what we did right, what we did wrong, and what we'd do differently. What we did right 1. Communicated immediately. The moment we knew we had a problem, we updated the status page and emailed our biggest customers personally. Not when we had answers. When we had a question. 2. Had a single incident commander. One person making calls. Not a committee. When the CEO tried to direct technical work, the IC politely rerouted and told her where her help was actually needed (talking to customers). 3. Took care of our people. During hour 4, I ordered food. During hour 5, I forced the primary engineer off the call for 20 minutes to walk outside. Long incidents destroy people. You have to feed them and force them to rest. 4. Wrote it down as we went. We had a shared doc with a live timeline. When the post-mortem came, we had every decision captured. What we did wrong 1. Tried to fix the root cause during the incident. For the first 2 hours, we were digging into why the database was struggling. We should have been mitigating (rolling back) first. 2. Let too many people 'help.' By hour 3, we had 12 engineers in the call. Half of them were useless. The IC should have kicked people out sooner. 3. Gave optimistic estimates. 'We'll be back in 30 minutes.' We were not back in 30 minutes. That miscommunication was worse than saying 'unknown.' 4. Didn't prepare the executive communication. The CEO had to answer customer questions in real time with no script. We should have drafted talking points for her after hour 1. What we'd do differently Mitigate first, investigate second. Always. Cap the number of active engineers at 4 during an incident. Others go on standby. Default to 'unknown' for estimates. Only give a number when we're sure. Assign someone explicitly to 'executive liaison.' Their job is to keep the C-suite informed without interrupting the tec

If someone asked one of the know AI chats for feedback on body, would it be honest or be supportive only submitted by /u/thowing_away48494578 [link] [留言]

submitted by /u/ThereWas [link] [留言]